Cardiac disease is the leading cause of death in the developed world. In an attempt to decrease cardiac fatalities and improve quality of life, a variety of diagnostic procedures have been developed for proactively diagnosing and treating cardiac conditions. In particular, various indicators of heart function are measured and reviewed in order to gain a better understanding of heart function, disorders, and the effects of various forms of treatment. The study of the left ventricle has become particularly important in this regard since the left ventricle is the portion of the heart that pushes oxygen-rich blood to the aorta for distribution throughout the body. Consequently, images and metrics of the left ventricle and left ventricular function provide valuable information concerning cardiac health.
For example, left ventricular stroke volume, i.e., the volume of blood pumped by the left ventricle through the aortic valve into the aorta, is an important clinical indicator for diagnosing cardiovascular disease and for monitoring the effects of treatment. The left ventricular ejection fraction, i.e., the ratio of the amount of blood in the left ventricle before a contraction to the amount of blood after a contraction, likewise provides important information to medical providers about the heart's function and the effect of therapy. The characteristics of the myocardium, which is a structure surrounding the left ventricle, may also provide important indicators of heart function. Imaging techniques can be used to study and evaluate the myocardium by ascertaining myocardial mass, myocardial thickness, and other characteristics.
Various existing imaging systems can provide detailed images of the heart. For example, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, and single photon emission computed tomography (SPECT) are all able to provide images of the heart. Many of these systems can produce three-dimensional images of their subjects, not only capturing the surface of the subject, but gathering details throughout the subject's interior.
An image can be viewed as a collection of units called “voxels.” A voxel is a building block that corresponds to a particular region of the image and includes information about the image such as color, intensity, and/or other characteristics. Because any imaging device does not have infinite precision, a voxel approximates the characteristics of the location in the subject which it represents. For example, while the area represented by a voxel may be shown to vary in intensity, an imaging device which cannot resolve smaller details may assign the voxel an intensity equal to what the imaging device measures over the entire area, such as the mean intensity of the area.
A voxel can have any number of dimensions, but typically has two, three, or four and each dimension can be spatial, temporal, or of another characteristic. In two spatial dimensions, a voxel is commonly known as a pixel and corresponds to a rectangular area of a two-dimensional image, the rectangular area typically corresponding to the smallest area able to be scanned by an imaging device, either because of internal settings or because of physical limitations, or the smallest area able to be displayed by a device for displaying the image. While a pixel represents a rectangular area of the subject, it may be displayed as a dot on a screen or paper or other display device. In three spatial dimensions, a voxel generally corresponds to a rectangular prism-shaped portion of the subject. Voxels can also include time dimensions if an imaging device scans a subject over a period of time.
Generally, three-dimensional images are made up of a composite of slices, each of which is a spatially two-dimensional image of a cross section of the subject. Typically, the slices are parallel and spaced a short distance apart so that the slices together form a reasonable approximation of the subject. A voxel in a three-dimensional image often has a length, measured normal to the slice, corresponding with the distance between consecutive slices. With a time dimension, each slice can comprise a series of frames, where a frame is the slice taken at a particular time, with a frame of a slice being similar in principle to a frame of a movie reel. Generally, the frames are captured with a short enough time between frames so that a reasonable representation of the subject over a time interval is achieved.
Multi-dimensional imaging gives medical providers a powerful tool as they are able to view the heart and the features of its interior over a period of time in order to view cardiac function in great detail. For instance, four-dimensional imaging allows medical providers to view a complete beat of a heart, from a diastole through a systole, in order to verify that the heart is functioning properly, to identify any problems or potential problems, or to determine whether drug or other therapy has had an effect.
In addition to the detailed images provided by various imaging systems, the images also allow medical providers to take important measurements relating to the subject. A radiologist or technician can review slices of the heart and trace the contours of a left ventricle or other structure, such as the myocardium, in each slice of the heart. Finding the area of the traced object in each slice allows estimation of the volume of the object as the distance between each slice is a known value. Moreover, when each slice comprises a series of frames, the volume can be watched over a period of time in order to determine important metrics, such as the ejection fraction discussed above. However, calculating the volume of an object in an image in this manner is very labor intensive as it requires a person to trace contours in each frame of each slice. Hand tracing also is subject to human error and physical limitations, especially if contours appearing in the image are not well defined or are too small to be seen without magnification.
Image processing systems have developed that allow automated and semi-automated analysis of image data provided from an imaging device. For instance, algorithms have been developed that are able to detect objects in an image, thereby reducing the amount of human intervention required for analysis of the image. For instance, algorithms for identifying the left ventricle in an image of a heart have been developed by which a person identifies a seed in the image, which is one or more points corresponding to blood in the left ventricle, and the algorithm grows a region from the seed by searching for similar points, such as points sharing a similar intensity. Often the algorithms start at the seed and grow outwardly, searching for neighboring points having an intensity or other attribute close to an intensity or other attribute of the seed. Points having similar characteristics are classified as corresponding to the left ventricle while points having dissimilar characteristics are classified as not corresponding to the left ventricle.
The intensity or other attributes of points in an image can also be used to locate other structures. For example, algorithms exist for identifying the outer (epicardial) border of the myocardium. Typically, these algorithms use one of several known schemes for detecting the myocardium, for example by using edge detection algorithms for finding the edge of the myocardium.
One method for identifying an epicardial border is through an active contour model (ACM). With an active contour model, a contour is strategically placed in an image, such as near an endocardial border. The initial contour is often a shape, such as a circle, corresponding to the general form of an object in the image. Initial contours can also be drawn in by hand. Gradients or other calculations based on the image are calculated and the contour is iteratively deformed according to the gradients until deformation of the contour ceases at the target border.
While they are generally helpful, existing methods for identifying and analyzing cardiac structures from image data have many disadvantages, and such disadvantages are often exacerbated by certain features of the heart. One such disadvantage is the inability of existing methods to accurately handle irregular structures. For example, the outer edge of the left ventricle at the basal or apical ends is often irregular in shape and does not have a generally circular outer edge as in mid-ventricular portions of the left ventricle. Existing methods also require significant user intervention in order to achieve a desirable level of accuracy. User intervention reduces the speed at which analysis can proceed, and also increases the associated personnel costs.
Moreover, existing methods characterize voxels as belonging to one structure or another, but do not take into account that a voxel may correspond to a region of a subject that contains part of more than one object, therefore causing inaccuracies in any related computations. For example, voxels at the outer surface of the left ventricle may correspond to a region of the heart containing both part of the left ventricle and part of the myocardium surrounding the left ventricle. A computation that assumes that such voxels correspond only to the left ventricle or only to the myocardium will be inaccurate. High accuracy measurements are crucial for diagnosing patients, especially those with actual or potential cardiomyopathy. Such conditions include heart failure, coronary artery disease, and poorly controlled high blood pressure (hypertension). Also, patients taking drugs with possible cardiotoxicities must be accurately measured. Cardiotoxic drugs include cancer drugs including anthracyclines, which in turn include doxorubicin. Other diseases and conditions may be cardiotoxic and may require careful monitoring of ejection fraction. Such diseases and conditions include: viral, bacterial, fungal, or parasitic infection; amyloidosis; chronic or long-term alcohol use; diabetes; thyroid disease, such as hyperthyroidism; thiamine and vitamin B deficiency; and genetic defects. Finally, existing methods are computationally intense, often requiring special hardware in order to perform all the necessary calculations in a reasonable amount of time.